# "Hiding in Plain Sight": Designing Synthetic Dialog Generation for Uncovering Socially Situated Norms

arXiv: https://arxiv.org/abs/2410.00998 · HTML: https://mlsanity.com/paper/2410.00998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2024.

## Abstract (arXiv)

Naturally situated conversations encapsulate the social norms inherent to their context, reflecting both the relationships between interlocutors and the underlying communicative intent. In this paper, we propose a novel, multi-step framework for generating dialogues that automatically uncovers social norms from rich, context-laden interactions through a process of self-assessment and norm discovery, rather than relying on predefined norm labels. Leveraging this framework, we construct NormHint, a comprehensive synthetic dialogue dataset spanning a wide range of interlocutor attributes (e.g., age, profession, personality), relationship types, conversation topics, and conversational trajectories. NormHint is meticulously annotated with turn-level norm violation information, detailed participant descriptions, and remediation suggestions-including alternative trajectories achieved through early intervention. Human validation and automated analysis demonstrate that our dataset captures diverse conversational topics with high naturalness and realism. Moreover, we discovered that fine-tuning a model with our norm violation data significantly enhances its ability to detect and understand potential norm violations in conversations.